mirror of
https://github.com/rmontanana/mdlp.git
synced 2025-08-15 23:45:57 +00:00
Ricardo Montañana
f0845c5bd1
Add some type casting to CPPFImdlp Add additional path to datasets in tests Fix some smells in sample Join CMakeLists
298 lines
10 KiB
C++
298 lines
10 KiB
C++
#include "gtest/gtest.h"
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#include "../Metrics.h"
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#include "../CPPFImdlp.h"
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#include <fstream>
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#include <iostream>
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#include "ArffFiles.h"
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#define EXPECT_THROW_WITH_MESSAGE(stmt, etype, whatstring) EXPECT_THROW( \
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try { \
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stmt; \
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} catch (const etype& ex) { \
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EXPECT_EQ(whatstring, std::string(ex.what())); \
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throw; \
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} \
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, etype)
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namespace mdlp {
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class TestFImdlp: public CPPFImdlp, public testing::Test {
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public:
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precision_t precision = 0.000001;
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TestFImdlp(): CPPFImdlp() {}
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string data_path;
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void SetUp()
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{
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X = { 4.7, 4.7, 4.7, 4.7, 4.8, 4.8, 4.8, 4.8, 4.9, 4.95, 5.7, 5.3, 5.2, 5.1, 5.0, 5.6, 5.1, 6.0, 5.1, 5.9 };
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y = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2 };
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fit(X, y);
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data_path = set_data_path();
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}
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string set_data_path()
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{
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string path = "../datasets/";
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ifstream file(path+"iris.arff");
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if (file.is_open()) {
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file.close();
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return path;
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}
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return "../../tests/datasets/";
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}
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void checkSortedVector()
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{
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indices_t testSortedIndices = sortIndices(X, y);
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precision_t prev = X[testSortedIndices[0]];
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for (unsigned long i = 0; i < X.size(); ++i) {
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EXPECT_EQ(testSortedIndices[i], indices[i]);
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EXPECT_LE(prev, X[testSortedIndices[i]]);
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prev = X[testSortedIndices[i]];
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}
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}
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void checkCutPoints(cutPoints_t& computed, cutPoints_t& expected)
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{
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EXPECT_EQ(computed.size(), expected.size());
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for (unsigned long i = 0; i < computed.size(); i++) {
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cout << "(" << computed[i] << ", " << expected[i] << ") ";
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EXPECT_NEAR(computed[i], expected[i], precision);
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}
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}
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template<typename T, typename A>
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void checkVectors(std::vector<T, A> const& expected, std::vector<T, A> const& computed)
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{
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ASSERT_EQ(expected.size(), computed.size());
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for (auto i = 0; i < expected.size(); i++) {
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EXPECT_NEAR(expected[i], computed[i], precision);
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}
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}
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bool test_result(samples_t& X_, size_t cut, float midPoint, size_t limit, string title)
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{
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pair<precision_t, size_t> result;
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labels_t y_ = { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 };
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X = X_;
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y = y_;
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indices = sortIndices(X, y);
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cout << "* " << title << endl;
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result = valueCutPoint(0, cut, 10);
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EXPECT_NEAR(result.first, midPoint, precision);
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EXPECT_EQ(result.second, limit);
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return true;
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}
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void test_dataset(CPPFImdlp& test, string filename, vector<cutPoints_t>& expected, int depths[])
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{
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ArffFiles file;
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file.load(data_path + filename + ".arff", true);
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vector<samples_t>& X = file.getX();
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labels_t& y = file.getY();
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auto attributes = file.getAttributes();
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for (auto feature = 0; feature < attributes.size(); feature++) {
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test.fit(X[feature], y);
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EXPECT_EQ(test.get_depth(), depths[feature]);
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auto computed = test.getCutPoints();
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cout << "Feature " << feature << ": ";
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checkCutPoints(computed, expected[feature]);
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cout << endl;
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}
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}
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};
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TEST_F(TestFImdlp, FitErrorEmptyDataset)
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{
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X = samples_t();
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y = labels_t();
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EXPECT_THROW_WITH_MESSAGE(fit(X, y), invalid_argument, "X and y must have at least one element");
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}
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TEST_F(TestFImdlp, FitErrorDifferentSize)
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{
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X = { 1, 2, 3 };
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y = { 1, 2 };
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EXPECT_THROW_WITH_MESSAGE(fit(X, y), invalid_argument, "X and y must have the same size");
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}
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TEST_F(TestFImdlp, FitErrorMinLengtMaxDepth)
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{
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auto testLength = CPPFImdlp(2, 10, 0);
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auto testDepth = CPPFImdlp(3, 0, 0);
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X = { 1, 2, 3 };
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y = { 1, 2, 3 };
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EXPECT_THROW_WITH_MESSAGE(testLength.fit(X, y), invalid_argument, "min_length must be greater than 2");
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EXPECT_THROW_WITH_MESSAGE(testDepth.fit(X, y), invalid_argument, "max_depth must be greater than 0");
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}
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TEST_F(TestFImdlp, FitErrorMaxCutPoints)
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{
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auto testmin = CPPFImdlp(2, 10, -1);
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auto testmax = CPPFImdlp(3, 0, 200);
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X = { 1, 2, 3 };
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y = { 1, 2, 3 };
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EXPECT_THROW_WITH_MESSAGE(testmin.fit(X, y), invalid_argument, "wrong proposed num_cuts value");
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EXPECT_THROW_WITH_MESSAGE(testmax.fit(X, y), invalid_argument, "wrong proposed num_cuts value");
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}
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TEST_F(TestFImdlp, SortIndices)
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{
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X = { 5.7, 5.3, 5.2, 5.1, 5.0, 5.6, 5.1, 6.0, 5.1, 5.9 };
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y = { 1, 1, 1, 1, 1, 2, 2, 2, 2, 2 };
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indices = { 4, 3, 6, 8, 2, 1, 5, 0, 9, 7 };
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checkSortedVector();
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X = { 5.77, 5.88, 5.99 };
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y = { 1, 2, 1 };
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indices = { 0, 1, 2 };
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checkSortedVector();
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X = { 5.33, 5.22, 5.11 };
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y = { 1, 2, 1 };
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indices = { 2, 1, 0 };
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checkSortedVector();
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X = { 5.33, 5.22, 5.33 };
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y = { 2, 2, 1 };
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indices = { 1, 2, 0 };
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}
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TEST_F(TestFImdlp, TestShortDatasets)
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{
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vector<precision_t> computed;
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X = { 1 };
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y = { 1 };
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fit(X, y);
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computed = getCutPoints();
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EXPECT_EQ(computed.size(), 0);
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X = { 1, 3 };
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y = { 1, 2 };
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fit(X, y);
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computed = getCutPoints();
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EXPECT_EQ(computed.size(), 0);
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X = { 2, 4 };
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y = { 1, 2 };
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fit(X, y);
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computed = getCutPoints();
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EXPECT_EQ(computed.size(), 0);
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X = { 1, 2, 3 };
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y = { 1, 2, 2 };
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fit(X, y);
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computed = getCutPoints();
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EXPECT_EQ(computed.size(), 1);
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EXPECT_NEAR(computed[0], 1.5, precision);
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}
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TEST_F(TestFImdlp, TestArtificialDataset)
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{
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fit(X, y);
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cutPoints_t expected = { 5.05 };
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vector<precision_t> computed = getCutPoints();
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int expectedSize = expected.size();
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EXPECT_EQ(computed.size(), expected.size());
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for (unsigned long i = 0; i < computed.size(); i++) {
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EXPECT_NEAR(computed[i], expected[i], precision);
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}
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}
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TEST_F(TestFImdlp, TestIris)
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{
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vector<cutPoints_t> expected = {
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{ 5.45, 5.75 },
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{ 2.75, 2.85, 2.95, 3.05, 3.35 },
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{ 2.45, 4.75, 5.05 },
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{ 0.8, 1.75 }
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};
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int depths[] = { 3, 5, 5, 5 };
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auto test = CPPFImdlp();
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//test_dataset(test, "iris.arff", expected, depths);
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}
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TEST_F(TestFImdlp, ComputeCutPointsGCase)
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{
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cutPoints_t expected;
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expected = { 1.5 };
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samples_t X_ = { 0, 1, 2, 2, 2 };
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labels_t y_ = { 1, 1, 1, 2, 2 };
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fit(X_, y_);
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auto computed = getCutPoints();
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checkCutPoints(computed, expected);
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}
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TEST_F(TestFImdlp, ValueCutPoint)
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{
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// Case titles as stated in the doc
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samples_t X1a{ 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4.0 };
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test_result(X1a, 6, 7.3 / 2, 6, "1a");
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samples_t X2a = { 3.1, 3.2, 3.3, 3.4, 3.7, 3.7, 3.7, 3.8, 3.9, 4.0 };
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test_result(X2a, 6, 7.1 / 2, 4, "2a");
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samples_t X2b = { 3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.8, 3.9, 4.0 };
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test_result(X2b, 6, 7.5 / 2, 7, "2b");
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samples_t X3a = { 3.1, 3.2, 3.3, 3.4, 3.7, 3.7, 3.7, 3.8, 3.9, 4.0 };
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test_result(X3a, 4, 7.1 / 2, 4, "3a");
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samples_t X3b = { 3.1, 3.2, 3.3, 3.4, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7 };
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test_result(X3b, 4, 7.1 / 2, 4, "3b");
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samples_t X4a = { 3.1, 3.2, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.9, 4.0 };
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test_result(X4a, 4, 6.9 / 2, 2, "4a");
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samples_t X4b = { 3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.8, 3.9, 4.0 };
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test_result(X4b, 4, 7.5 / 2, 7, "4b");
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samples_t X4c = { 3.1, 3.2, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7 };
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test_result(X4c, 4, 6.9 / 2, 2, "4c");
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}
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TEST_F(TestFImdlp, MaxDepth)
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{
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// Set max_depth to 1
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auto test = CPPFImdlp(3, 1, 0);
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vector<cutPoints_t> expected = {
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{ 5.45 },
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{ 3.35 },
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{ 2.45 },
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{0.8 }
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};
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int depths[] = { 1, 1, 1, 1 };
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test_dataset(test, "iris", expected, depths);
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}
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TEST_F(TestFImdlp, MinLength)
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{
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auto test = CPPFImdlp(75, 100, 0);
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// Set min_length to 75
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vector<cutPoints_t> expected = {
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{ 5.45, 5.75 },
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{ 2.85, 3.35 },
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{ 2.45, 4.75 },
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{ 0.8, 1.75 }
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};
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int depths[] = { 3, 2, 2, 2 };
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test_dataset(test, "iris", expected, depths);
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}
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TEST_F(TestFImdlp, MinLengthMaxDepth)
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{
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// Set min_length to 75
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auto test = CPPFImdlp(75, 2, 0);
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vector<cutPoints_t> expected = {
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{ 5.45, 5.75 },
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{ 2.85, 3.35 },
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{ 2.45, 4.75 },
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{ 0.8, 1.75 }
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};
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int depths[] = { 2, 2, 2, 2 };
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test_dataset(test, "iris", expected, depths);
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}
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TEST_F(TestFImdlp, MaxCutPointsInteger)
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{
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// Set min_length to 75
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auto test = CPPFImdlp(75, 2, 1);
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vector<cutPoints_t> expected = {
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{ 5.45 },
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{ 3.35 },
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{ 2.45 },
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{ 0.8}
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};
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int depths[] = { 1, 1, 1, 1 };
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test_dataset(test, "iris", expected, depths);
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}
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TEST_F(TestFImdlp, MaxCutPointsFloat)
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{
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// Set min_length to 75
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auto test = CPPFImdlp(75, 2, 0.2);
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vector<cutPoints_t> expected = {
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{ 5.45, 5.75 },
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{ 2.85, 3.35 },
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{ 2.45, 4.75 },
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{ 0.8, 1.75 }
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};
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int depths[] = { 2, 2, 2, 2 };
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test_dataset(test, "iris", expected, depths);
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}
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TEST_F(TestFImdlp, ProposedCuts)
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{
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vector<pair<float, size_t>> proposed_list = { { 0.1, 2}, { 0.5, 10}, {0.07, 1}, {1, 1}, {2, 2} };
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size_t expected, computed;
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for (auto proposed_item : proposed_list) {
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tie(proposed_cuts, expected) = proposed_item;
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computed = compute_max_num_cut_points();
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ASSERT_EQ(expected, computed);
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}
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}
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}
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